1. Field of the Invention
The present invention relates to a multidimensional multi-valued color image compression and decompression method, and more specifically to the multidimensional multi-valued color image compression and decompression method of compressing and decompressing image information effectively in such a system that the correspondence between the compression side and the decompression side is not guaranteed with respect to pixels or frames on time axis, as with the case of the image transmission between two image system of different models.
2. Description the Related Art.
When image signals are converted into digital image signals on the basis of linear quantization (uniform quantization), in general when a difference between a representative point and an original point is required to be not noticed in a natural image, it is necessary to use the number of bits from 6 bits (64 gradations) to 8 bits (256 gradations) for each sample value of the image signals. Therefore, when the image signals digitalized on the basis of uniform quantization are recorded as they are, a great amount of image information must be processed.
To code the image signals on the basis of a lesser amount of image information, various methods of compressing the image information, that is, the various methods of efficient coding have been so far proposed.
For instance, there exists such a method of utilizing the human nature as to the sense of sight or hearing such that the human sensitivity is high when a change is gentle but low when a change is violent, whenever audio or video signals change.
Or else, there exists such a method of utilizing correlation between image information values on the time-or space-axis. In this method, using high correlation in luminance value between adjacent pixels, a small number of approximation values of the original information are transmitted; or differences in image signal between pixels or between frames are transmitted; or the frequency components are reduced on the basis of the fact that the high frequency components are less.
As described above, after the amount of information for each sample value has been reduced, the digital data are recorded, transferred or transmitted. Further, after having received and reproduced the digital data (whose amount of information has been already compressed) are decompressed for restoration of the compressed digital data to the original
The above-mentioned various methods have been so far executed and thereby well known.
In the above-mentioned prior art image information compression methods, however, a main stress has been so far laid on how to restore the decomposed image signals at the respective pixels under excellent conditions. In this case, however, the compression method is usually established under the conditions that the number of pixels of the original image (on the compression side) matches that of the restored image (on the decompressed images). As a result, when the compression and decompression operation is effected between images of different numbers of pixels, it has been so far necessary to additionally interpolate or reduce the number of the pixels after decompression.
In the prior art image information compression method, this implies that only true effective information is not necessarily extracted end then restored, but that the image information is dependent, to same extent, upon the physical image constituting elements, such as the number of pixels, pixel shapes and luminance level.
On the other hand, as an example in which the number of pixels is extremely different from each other between original image and the decompressed image, there exists the case where an image photographed by an image sensing device is required to be used as a print block copy. In this case, the pixel density of an image obtained by sensing device is about (500.times.500) per frame at the most. On the other hand, the pixel density of an image printed by an electronic photoengraving machine is about (several thousands.times.several thousands) per frame, which is extraordinarily larger than that of the image sensing device. As a result, even if the compression and decompression method is not at all adopted to the image information, aliasing occurs due to the enlargement of the pixels.
Further, when interpolation is only effected without enlarging the pixels, the weighted mean values of known data must be allocated to a wide interpolation area, so that the image inevitably deteriorates due to interpolation distortion.
In contrast with this, in the case where the pixel density of the original image is as large as (several thousands.times.several thousands), since the correlation between adjacent pixels is extremely high, it is possible to compress the image information at a high compression ratio in principle.
In this case, however, in the prior art image information compression and decompression method established under the conditions that the number of pixels of the original image (on the compression side) matches that of the restored image (on decompression side), there arises such a drawback that it is impossible to increase the compression ratio.
To overcome this problem, the applicant of the present invention has already proposed a multidimensional multi-valued compression and decompression method suitable for when the number of pixels of the original image does not match that of the reproduced image, in Japanese Patent Application No. 5-39492 (in 1993).
This method can be summarized as follows: first equi-luminance contour lines are obtained on the basis of image information only feature points (feature pixels) of the image are extracted on the basis of the equi-luminance contour lines to obtain compressed image data; and the feature point positions and the luminance values at these feature points are both transferred and recorded. In the decompression of the image information, the luminance values at pixels other than the feature points are decided on the basis of an interpolation plane decided by the feature points and a plurality of adjacent feature points. Here, the feature points are the negative or positive maximum points of each of the curvatures of the equi-luminance contour lines, for instance. Or else, the feature points are decided at positions where a difference between a straight line (obtained by approximating the equi-luminance contour line) and the equi-luminance contour line exceeds a predetermined threshold, respectively.
On the other hand, in order to obtain the equi-luminance contour lines, it is necessary to trace the pixels having a specific luminance value. In the above-mentioned related image tracing method, the centers of the boundary pixels are traced, and a chain code train has been adopted. In this case, there exist problems in that an error is produced at a contour between the original image and the reproduced image or in that a long processing time is required because the amount of information to be process is huge.
Further, when the luminance values are binarized on the basis of a luminance threshold to extract the equi-luminance line, if the distribution of extracted equi-luminance line forms a narrow triangular shape for instance, there exists such a case that a white region with a single pixel width appears. Under these conditions, when the centers of the boundary pixels are traced, there exists a case where a center of one pixel appears twice. As a result, when a character is enlarged, there exists problem in that the white region remains as a straight line with a single pixel width.
To overcome this problem, the applicant of the present invention has disclosed a binary image contour tracing method, in Japanese Laid-Open Patent Application No. 5-35872 (in 1993).
In this binary image contour tracing method, the tracing direction of a boundary point of a pixel is defined on the basis of white and black at four pixels around the boundary point. In more detail, the tracing direction is determined in such a direction that black pixels are present on the right side and white pixels are present on the left side. In this case, when the tracing direction is upward in an image stood vertically, the direction decided as [1]; when leftward, the direction is decided as [2]; and when downward, the direction is decided as [3]; and when rightward, the direction is decided as [0], respectively.
The above-mentioned directions are allocated to the respective bits of 4-bit tracing direction flags. In addition, the above tracing direction flags are attached to all the boundary points of the image, by setting bit [1] to the boundary points to be traced and [0] to the boundary points to be not traced.
Further, the tracing direction flag at a boundary point already traced is changed, by retrieving a boundary point on the basis of the tracing direction flag and by tracing another boundary point in the tracing direction beginning from the retrieved boundary point as a start point. The above-mentioned tracing is effected until the all the tracing direction flags change to [0000], that is, until the image contour can be obtained.
In the above-mentioned binary image contour tracing method, it is possible to trace the boundary pixels (boundary points) by only allocating the previously defined tracing directions to an inputted image. In other words, since it is unnecessary to retrieve the tracing direction for each pixel, it is possible to eliminate a large capacity memory unit and further to shorten the processing time thereof.
In this related method, however, since the image contour is traced along the boundary lines of pixels by deciding the tracing direction in such a way that the black pixel is present on the right side and the white pixel is present on the left side along the tracing direction, when the reproduced image is observed, there exists a problem in that a black pixel is produced for each pixel on the left side along the tracing direction. This causes no problem when the original image is a character, for instance. However, when the original image is an ordinary image, this causes a deterioration of image quality.
As a result, when the above-mentioned binary image contour tracing method is applied to the already proposed multidimensional image compression and decompression method, a method of solving the above-mentioned problems is needed.
In addition, in the already proposed multidimensional image compression and decompression method, the equi-luminance contour lines (an equi-luminance plane in the case of three dimensions) are approximated by a polygonal shape on the basis of the extracted feature points. Further, the luminance information at pixels other than the feature points are decided on the basis of an interpolation plane (an interpolation solid in the case of three dimensions) decided by a plurality of adjacent feature points.
The above-mentioned equi-luminance contour line is different from the original equi-luminance contour line because the information values are compressed. Therefore, there exists a problem in that the order of the luminance intensities is reversed between the two equi-luminance contour lines at some points, with the result that the luminance distribution of the reproduced image is not normal and thereby the image quality deteriorates. A method of solving this problem has been also required.
Further, when the reproduced image is of color image, the color image is usually divided into three primary color image components, or into a luminance component and two chrominance components, before the color image is recorded, reproduced, transmitted and processed. Similarly, in the case of the compression end decompression of the color images, it is conventional to code and decode the above-mentioned respective signal components separately. However, when the luminance component and the chrominance components are coded and decoded separately in the color image compression and decompression, since two different codes are allocated to the luminance component and the chrominance components, respectively, it is impossible to reduce the amount of codes below a constant value.
Therefore, it is impossible to solve the above-mentioned problem by simply applying the image compression and decompression technique, as proposed by the Japanese Patent Application No. 5-39492, to the conventional color image compression and decompression processing. On the other hand, when the respective feature points are decided by extracting the respective equi-luminance lines for each of a plurality of image components for constituting the color image, there arises the other problem in that a number of feature points increases and further color shearing is produced between the image components due to the polygonal approximation executed for the information compression.